1. Field of Invention
This invention relates to the measurement of biological functions and the related anatomy.
2. Description of Prior Art
Doctors always need better tools for visualizing anatomy and evidences of disease. X-Ray, CT-scans (computer tomographic), MRI (magnetic resonance imaging), and thermography (infra read thermal imaging) serve different niches of this need. Each brings different capabilities. This discussion begins with thermography, as the most relevant of the prior art to the current invention, and then reviews the limited existing applications of time-series imagery in other types of imaging.
Thermographic mammography has drawn interest for detecting breast cancer. It not only avoids the potentially harmful effects of X-ray exposure, but also eliminates the need for squeezing the breast into a position that allows X-rays to bypass the chest wall. It has been suggested that this squeezing may itself contribute to the development of disease. But, the far more relevant risk is that the unpleasantness of the experience prevents many women from seeking adequate screening and diagnosis. The best test in the world is of no use if the patient will not consent to it. A primary benefit of thermography is increasing patient cooperation by eliminating this squeezing.
But existing thermographic techniques are limited by several factors. First, the abnormal vascularization typical of cancer can only be detected indirectly, by local changes in temperature. Thermography senses temperature differences by the relative magnitude of the received infra red (IR) radiation from different areas of the patient. These changes must be found in the presence of the IR emissions from healthy tissues, referred to here as background clutter. As many researchers have discovered, the variations across healthy tissue are so large that is extremely difficult to distinguish them from anomalies due to disease. IR thermography is further complicated by limited tissue penetration due to absorption and scattering of the IR photons emitted from sub-surface tissues. This severely attenuates images of features or problems below the skin surface.
Perhaps the best of the existing thermographic methods for overcoming these problems, due to Dr. George E. Chapman of San Diego, is based on the mammalian “dive” reflex. This reflex causes blood flow to the skin and extremities to shut down under thermal stress, as when sea-based mammals dive into deeper and colder water. This reflex is familiar to skiers as the cause of exaggerated cold in the fingers and toes, relative to the rest of the body.
Chapman uses the dive reflex constructively by placing the patient in a cold room and collecting a baseline thermogram (infra red image). Then, additional thermal stress is applied by placing the patient's hand in ice water. Another thermogram is taken under this stress. The difference between these thermograms shows the degree of dive reflex response. In healthy tissue, the response is severe.
However, this dive response is severely attenuated by the presence of cancer. The chemicals which cause angiogenesis (blood vessel growth) in cancer also attenuate the dive reflex. This attenuation occurs over an extended volume of tissue, so that deeply buried tumors can be detected by observing the skin surface. Lack of the usual dive response indicates cancer.
Thermal variations in healthy tissue are doubly suppressed by this method. First, they are suppressed by chilling the patient, decreasing the overall background clutter level. They are further suppressed by looking at the difference between the baseline thermogram and the stress thermogram. Common spatial features in the background cancel. This removes most of the clutter due to contrasts in healthy tissue.
While this method has proven quite useful for cancer detection, the very nature of the observed phenomena makes it less adequate for localization and for finding other anomalies. It is good for detecting tumors, but other techniques are then needed to determine their exact location and size. And, while the discomfort to the patient is far less than with conventional X-ray approaches, the cold room and icy finger baths are still unpleasant.
And, regardless of this method's effectiveness, it looks for only one phenomenon. It uses only still images, from before and after the thermal stress (the significance of this limitation is developed in the following discussion). A proper diagnostic arsenal contains multiple methods based upon different phenomenologies. Additional methods that detect different characteristics of disease are always sought.
Others have taken different approaches to suppressing background clutter. Most are based on different variations on the theme of subtracting a reference background. Sclager (U.S. Pat. Nos. 4,186,748 and 4,310,003) limits the effect of IR background levels and variations in healthy tissue by examining thermal difference between the patient's left and right breasts. The principle is that disease anomalies tend to be highly asymmetrical compared to normal tissue. This method uses pairs of images taken under static conditions.
Use of temporal (time series) sequences to suppress random noise is well established in the prior art. However, this is not by itself, useful for suppressing clutter that is essentially static over time, which is the problem discussed here.
Zheng, et al (U.S. Pat. No. 6,447,460) looks for static characteristics, such as thermal shading, to find deep vein thrombosis (blood clots). This method looks for anomalies without using baseline image differencing, and uses only still images. It works because it is looking for pronounced thermal differences relative to the background clutter.
Cohenford (U.S. Pat. No. 6,620,621) uses an active illumination rather than passive imaging, looking for specific absorption characteristics vs. wavelength to identify anomalies. A time series of images is used, but this is strictly for the purposes of forming a spectrogram, assessing differences in absorption vs. wavelength for different parts of the tissue as the stimulation varies with time. It looks only for static, not time changing, characteristics of the tissue. These techniques are typically not useful in vivo. And again, no attempt is made to use temporal characteristics of the disease process.
Jenni (U.S. Pat. No. 6,601,459) and variety of other ultra-sound solutions use Doppler in order to directly detect localized blood flow. But there still is no attempt to use temporal variations due to disease processes. While Doppler does represent motion, the velocity is used as a static feature, like intensity in a thermogram. No attempt is made to characterize time varying parameters across the image.
Keren, (U.S. Pat. No. 6,574,500) makes very limited use of time series imagery, but for a completely different purpose. Keren looks at a sequence of images to find the most stable sample, by finding the image most like the images sampled near it. This is done to suppress temporal changes, not to highlight them. Selecting the most stable frame provide the greatest difference vs. a later image taken after a contrast agent is injected. Temporal variation is only used as a diagnostic signature in terms of before and after the agent is injected, again comparing two static conditions. This general trend of attempting to suppress temporal variations is repeated elsewhere. Time series data is used only to avoid variations, not to capitalize upon them.
Heuscher, et al (U.S. Pat. No. 6,154,516) and Yavuz, et al. (U.S. Pat. No. 6,539,074) deal with temporal image variations primarily as an impediment to computer tomographic (CT) imaging. They use CT image samples synchronized to the phase of the patient's heartbeat to suppress motion variation/smear, thereby to produce crisp images. Heuscher focuses on creating an image at a single phase of the heartbeat, in order to cancel unintended motion.
Yavuz further suggests the possibility of examining the 3D motion sequence of the heart pumping as a diagnostic tool. But, this still is in the context of watching a moving object. Prior art does not discuss extracting specific temporal features from fixed localized areas.
Minkin (U.S. Pat. No. 6,668,071) explicitly uses temporal variations in an image to find a patient's pulse. But, only in terms of detecting the pulse as a global phenomenon across the image, in much the same sense that a stethoscope measures a single value versus time. No attempt is made to sense localized variations in the temporal response.